False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid
This study investigates the vulnerability of dc microgrid systems to cyber threats, focusing on false data injection attacks (FDIAs) affecting sensor measurements. These attacks pose significant risks to equipment, generation units, controllers, and human safety. To address this vulnerability, we pr...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10540225/ |
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| author | Md Abu Taher Milad Behnamfar Arif I. Sarwat Mohd Tariq |
| author_facet | Md Abu Taher Milad Behnamfar Arif I. Sarwat Mohd Tariq |
| author_sort | Md Abu Taher |
| collection | DOAJ |
| description | This study investigates the vulnerability of dc microgrid systems to cyber threats, focusing on false data injection attacks (FDIAs) affecting sensor measurements. These attacks pose significant risks to equipment, generation units, controllers, and human safety. To address this vulnerability, we propose a novel solution utilizing a nonlinear autoregressive network with exogenous input (NARX) observer. Trained to differentiate between normal conditions, load changes, and cyber-attacks, the NARX network estimates dc currents and voltages. The system initially operates without FDIAs to collect data for training NARX networks, followed by online deployment to estimate output dc voltages and currents of distributed energy resources. An attack mitigation strategy using a proportional–integral controller aligns NARX output with actual converter output, generating a counter-attack signal to nullify the attack impact. Comparative analysis with other AI-based methods is conducted, demonstrating the effectiveness of our approach. MATLAB simulations validate the method's performance, with real-time validation using OPAL-RT further confirming its applicability. |
| format | Article |
| id | doaj-art-92d79d373dbc430db417baa9cb5e7e80 |
| institution | Kabale University |
| issn | 2644-1284 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj-art-92d79d373dbc430db417baa9cb5e7e802025-01-17T00:01:24ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842024-01-01544145710.1109/OJIES.2024.340622610540225False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC MicrogridMd Abu Taher0https://orcid.org/0000-0002-7136-179XMilad Behnamfar1https://orcid.org/0009-0003-0284-7221Arif I. Sarwat2https://orcid.org/0000-0003-1179-438XMohd Tariq3https://orcid.org/0000-0002-5162-7626Florida International University, Miami, FL, USAFlorida International University, Miami, FL, USAFlorida International University, Miami, FL, USAFlorida International University, Miami, FL, USAThis study investigates the vulnerability of dc microgrid systems to cyber threats, focusing on false data injection attacks (FDIAs) affecting sensor measurements. These attacks pose significant risks to equipment, generation units, controllers, and human safety. To address this vulnerability, we propose a novel solution utilizing a nonlinear autoregressive network with exogenous input (NARX) observer. Trained to differentiate between normal conditions, load changes, and cyber-attacks, the NARX network estimates dc currents and voltages. The system initially operates without FDIAs to collect data for training NARX networks, followed by online deployment to estimate output dc voltages and currents of distributed energy resources. An attack mitigation strategy using a proportional–integral controller aligns NARX output with actual converter output, generating a counter-attack signal to nullify the attack impact. Comparative analysis with other AI-based methods is conducted, demonstrating the effectiveness of our approach. MATLAB simulations validate the method's performance, with real-time validation using OPAL-RT further confirming its applicability.https://ieeexplore.ieee.org/document/10540225/Control system resiliencecyber-attacksDC Microgridfalse data injection attacks (FDIAs)nonlinear autoregressive network with exogenous input (NARX)-based observerOPAL-RT |
| spellingShingle | Md Abu Taher Milad Behnamfar Arif I. Sarwat Mohd Tariq False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid IEEE Open Journal of the Industrial Electronics Society Control system resilience cyber-attacks DC Microgrid false data injection attacks (FDIAs) nonlinear autoregressive network with exogenous input (NARX)-based observer OPAL-RT |
| title | False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid |
| title_full | False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid |
| title_fullStr | False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid |
| title_full_unstemmed | False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid |
| title_short | False Data Injection Attack Detection and Mitigation Using Nonlinear Autoregressive Exogenous Input-Based Observers in Distributed Control for DC Microgrid |
| title_sort | false data injection attack detection and mitigation using nonlinear autoregressive exogenous input based observers in distributed control for dc microgrid |
| topic | Control system resilience cyber-attacks DC Microgrid false data injection attacks (FDIAs) nonlinear autoregressive network with exogenous input (NARX)-based observer OPAL-RT |
| url | https://ieeexplore.ieee.org/document/10540225/ |
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